RESEARCH ARTICLE


Spatially Predictive Habitat Modeling of a White Stork (Ciconia Ciconia) Population in Former East Prussia in 1939



Claudia Wickert*, 1, Dieter Wallschlager2, Falk Huettmann3
1 Ecoethology Lab, University of Potsdam, Maulbeerallee 2a, 14469 Potsdam, Germany
2 Ecoethology Lab, University of Potsdam, Maulbeerallee 2a, 14469 Potsdam, Germany
3 EWHALE Lab, Biology and Wildlife Department, Institute of Arctic Biology, University of Alaska-Fairbanks, Fairbanks Alaska 99775-7000, USA

Article Information


Identifiers and Pagination:

Year: 2010
Volume: 3
First Page: 1
Last Page: 12
Publisher Id: TOOENIJ-3-1
DOI: 10.2174/1874453201003010001

Article History:

Received Date: 03/08/2009
Revision Received Date: 10/10/2009
Acceptance Date: 15/10/2009
Electronic publication date: 9/3/2010
Collection year: 2010

© 2010 Wickert et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Ecoethology Lab, University of Potsdam, Maulbeerallee 2a, 14469 Potsdam, Germany; Tel: (907) 474-7882; Fax: (907) 474-6716; E-mail: cla_wi@hotmail.com


Abstract

Historic information is often crucial for assessing changes and drivers for wildlife and habitat changes although it is often plagued with statistically poor quality. Here we developed three habitat models on two different scales for 1939 for the white stork (Ciconia ciconia) in the region of former East Prussia. We used a geographical information system and a statistical modeling algorithm that comes from the disciplines of machine-learning and data mining (TreeNet). The occurrence of white stork nesting grounds is mainly defined by the variables ‘distance to forest’, ‘distance to/density of settlement’, ‘distance to pasture’ and ‘distance to coastline’. The models present for the first time a quantitative predictive distribution estimate for East Prussia. They are a sound foundation but could be further improved by more data regarding the structure of the habitat and more exact spatially explicit information on the location of white stork nesting sites.

Keywords: Stochastic gradient boosting, TreeNet, Predictive GIS habitat model, White stork (Ciconia ciconia), East Prussia.